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Non-parametric approximate linear programming for MDPs

dc.contributor.advisor Parr, Ronald
dc.contributor.author Pazis, J
dc.contributor.author Parr, R
dc.date.accessioned 2013-01-16T20:47:45Z
dc.date.issued 2011-11-02
dc.identifier.uri http://hdl.handle.net/10161/6189
dc.description.abstract The Approximate Linear Programming (ALP) approach to value function approximation for MDPs is a parametric value function approximation method, in that it represents the value function as a linear combination of features which are chosen a priori. Choosing these features can be a difficult challenge in itself. One recent effort, Regularized Approximate Linear Programming (RALP), uses L1 regularization to address this issue by combining a large initial set of features with a regularization penalty that favors a smooth value function with few non-zero weights. Rather than using smoothness as a backhanded way of addressing the feature selection problem, this paper starts with smoothness and develops a non-parametric approach to ALP that is consistent with the smoothness assumption. We show that this new approach has some favorable practical and analytical properties in comparison to (R)ALP. Copyright © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
dc.format.extent 459 - 464
dc.relation.ispartof Proceedings of the National Conference on Artificial Intelligence
dc.title Non-parametric approximate linear programming for MDPs
dc.type Journal article
dc.department Computer Science
pubs.organisational-group Duke
pubs.organisational-group Duke
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group Duke
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group Computer Science
pubs.publication-status Published
pubs.volume 1


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